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The Economic Value of Targeting Aging

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The Economic Value of Targeting Aging ANALYSIS https://doi.org/10.1038/s43587-021-00080-0 The economic value of targeting aging Andrew J. Scott 1 ✉ , Martin Ellison 2 and David A. Sinclair 3 Developments in life expectancy and the growing emphasis on biological and ‘healthy’ aging raise a number of important ques- tions for health scientists and economists alike. Is it preferable to make lives healthier by compressing morbidity, or longer by extending life? What are the gains from targeting aging itself compared to efforts to eradicate specific diseases? Here we analyze existing data to evaluate the economic value of increases in life expectancy, improvements in health and treatments that target aging. We show that a compression of morbidity that improves health is more valuable than further increases in life expectancy, and that targeting aging offers potentially larger economic gains than eradicating individual diseases. We show that a slowdown in aging that increases life expectancy by 1 year is worth US$38 trillion, and by 10 years, US$367 trillion. Ultimately, the more progress that is made in improving how we age, the greater the value of further improvements. ife expectancy (LE) has increased dramatically over the past rates, retirement age, and knowledge of remaining LE and likely 150 years1, although not all of the years gained are healthy. future health. Changes in health or longevity lead to changes in these LAnalysis of the Global Burden of Disease dataset2 suggests that economic decisions, enabling us to estimate an individual’s willing- the proportion of life in good health has remained broadly con- ness to pay (WTP) for these improvements. WTP is measured in US stant, implying increasing years in poor health. Furthermore, the dollars and reflects the increase in VSL induced by improvements in disease burden is shifting towards chronic non-communicable dis- health and longevity. VSL is the sum of the value of each remaining eases, estimated to have caused 72.3% of deaths in the United States year of life, discounted to the present day and weighted by the sur- in 2016. The result is “a substantial part of life, and certainly most vival rate. As the value of each year of life depends on health, con- deaths, now occur in a period in the lifespan when the risk for frailty sumption and leisure, the VSL incorporates both the quantity and and disability increases exponentially.”3 As a consequence, there is quality of expected life remaining. Importantly, this means that the a growing emphasis on ‘healthy aging’ and an emerging body of VSL is higher than an individual’s lifetime income; life is valuable research focusing on the biology of aging (see refs. 4,5). According in its own right because individuals value time, health and leisure. to another paper, “this era marks an inflection point, not only in The demographic data underpinning our analysis are: (1) a sur- aging research but also for all biological research that affects the vival function12, in which mortality risk increases exponentially human healthspan.”6 with age; (2) a health deficit function13, which also increases expo- These developments pose a number of important questions. Is it nentially with age; and (3) the 2017 population structure and birth preferable to make lives healthier by compressing morbidity, or lon- rates from the US Census Bureau. In our baseline calculations, LE ger by extending life? What are the gains from targeting aging itself, and healthy life expectancy (HLE) at birth are 78.9 and 68.5 years, with its potential to make lives both healthier and longer? How does respectively, based on current US data. Following ref. 14, we set the the value of treating aging compare to eradicating specific diseases? average VSL of an adult aged between 25 and 65 years to US$11.5 How will the economic value of these gains evolve over time? To million. Although our dollar WTP values are sensitive to the precise answer these questions, we take an economic rather than biologi- calibration of our model, the relative importance of different treat- cal perspective. Specifically, we use the value of statistical life (VSL) ments for aging is not. methodology to place a monetary value on the gains from longer life, better health, and changes in the rate at which we age7–9. Life extension (the Struldbrugg case). We first focus on improv- VSL models have two distinct advantages for our purposes. First, ing LE which, with reference to Gulliver’s Travels15, we refer to as they are already used by government agencies to evaluate different the Struldbrugg case. Struldbruggs, both male and female, are policy measures and treatments, for example10,11. Second, as our born immortal but age normally, so live in continuously worsen- model is based around optimizing economic agents, we can calcu- ing health. In our simulations, we achieve this by reducing the rate late not only the current gains from targeting aging but also how at which mortality increases with age while holding unchanged the these gains will evolve in response to potential future changes in rate at which health declines. The result is an expansion of morbid- health and LE. The results reveal a distinctive feature of age-target- ity such that the ratio of HLE to LE deteriorates. ing treatments. Interactions between health, longevity, economic The WTP for LE increases depends on which years benefit from decisions and demographics create a virtuous circle, such that the lower mortality. To provide consistency across simulations, we more successful society is in improving how we age, the greater the assume mortality is subject to a compensating effect16 whereby it economic value of further improvements. reaches a rate M at age T. Under this specification, there are two ways to extend LE. The first is to rectangularize the survival func- Results tion such that M and T are kept constant but mortality falls at all Our economic model is based on that in ref. 8 and calibrated to cur- ages less than T while rising more rapidly at T (Fig. 1, blue survival rent US data. In the model, individuals make choices about con- function). The second involves increasing lifespan, such that mor- sumption, hours worked and leisure based on wage rates, interest tality reaches M at higher values of T. In this case, survival rates 1Department of Economics, London Business School, London, UK. 2Department of Economics, University of Oxford, Oxford, UK. 3Harvard Medical School, Boston, MA, USA. ✉e-mail: [email protected] 616 NatURE AGING | VOL 1 | JULY 2021 | 616–623 | www.nature.com/nataging NATURE AGING ANALYSIS 1.00 0.90 0.80 0.70 0.60 0.50 Survival rate 0.40 0.30 0.20 0.10 0.00 147101316192225283134374043464952555861646770737679828588919497100 103106 109112115118 121 Age (years) Baseline Compression through rectangularization Lifespan improvement Fig. 1 | Survival functions under rectangularization and improvement in lifespan. Three different survival functions (probability of surviving from birth to different ages). Baseline is a standard Gompertz–Makeham survival function calibrated to 2019 US data. The rectangularization curve shows a survival function in which improvements in LE are achieved through compressing morbidity. The lifespan improvement curve shows a survival function in which improvements arise through elongation of the aging process. Table 1 | WTP for 1-year increases in remaining LE (the Struldbrugg case) Age at which WTP is calculated (years) 0 20 40 60 80 R L R L R L R L R L First 118.1 96.5 171.0 141.5 232.0 199.4 285.6 257.7 312.2 288.1 Second 114.1 93.4 165.5 137.1 226.1 193.2 279.7 250.0 304.4 278.1 Third 110.0 90.4 160.1 132.7 220.0 187.2 273.8 242.1 298.0 268.5 Fourth 105.9 87.5 154.5 128.4 213.8 181.3 267.7 234.6 292.0 259.4 Fifth 101.8 84.6 148.9 124.3 207.4 175.6 261.5 227.4 286.0 250.7 Tenth 81.8 71.5 120.8 105.0 173.0 149.1 226.2 193.8 225.3 212.2 Twentieth 50.3 74.0 105.9 139.3 152.7 Thirtieth 35.0 51.6 74.3 98.8 109.5 WTP for the first, second and further 1-year increases in remaining LE at ages 0, 20, 40, 60 and 80 years. The LE remaining at these ages in the baseline simulation is 78.9, 59.0, 39.5, 21.7 and 8.4 years. Some values are missing because there is an upper limit to how much LE can be extended through rectangularization. Values are given in US$1,000. L, improvements in lifespan T; R, rectangularization. decline more slowly, increasing the probability of living beyond T 79.9 years via rectangularization is US$118,100, the WTP at age 60 (Fig. 1, yellow survival function). for the first 1-year increase in remaining LE from 21.7 to 22.7 years Table 1 shows the WTP for increases of 1 year in remaining LE through lifespan improvement is US$257,700. The subsequent rows through rectangularization versus improvements in lifespan, at ages (Second, Third, and so on) show the WTP for additional 1-year 0, 20, 40, 60 and 80 years. The first row is the WTP for the initial increases, so in the row starting ‘Tenth’, the WTP at age 20 shows an (First) 1-year increase from our baseline; for example, the WTP at increase in remaining LE from 68.0 to 69.0 years, since by the tenth birth for the initial 1-year increase in remaining LE from 78.9 to increment the remaining LE at age 20 has already risen to 68.0 years.
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